Predicting oil and gas reservoir production

ABSTRACT

A method of predicting an output of oil and gas production in a hydrocarbon reservoir of a current and future producing well using a neural network model, includes receiving a data set comprising a plurality of parameters of the hydrocarbon reservoir at a wellsite. The method also includes using the data set to generate a plurality of simulation curves of the hydrocarbon reservoir, each parameter of the plurality of parameters has a range, and the range is adjustable, and the wellsite includes a wellbore penetrating a subterranean formation to extract reserves from the hydrocarbon reservoir. The method also includes performing a simulation, based on the range of each said parameter of the plurality of parameters, of the hydrocarbon reservoir. The method also includes downloading the plurality of simulation curves into a local server to prepare training data for training the neural network model.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. patent application Ser.No. 63/262,289 filed on Oct. 8, 2021, which is hereby incorporated byreference in its entirety herein.

BACKGROUND

Oil and gas reservoirs are underground formations of rock containing oiland/or gas. The type and properties of the rock vary by reservoir. Anoil or gas reservoir is a zone in the earth that contains sources of oiland gas. When a reservoir is found, one or more wells are drilled intothe earth to tap into the source(s) of oil and gas for bringing thesources to the surface.

In some instances, the surface is an onshore or offshore facilityproducing conventional or unconventional hydrocarbons from asubterranean reservoir. Some hydrocarbons applies to oil and gasresources which, in some instances, are easily extracted, after thedrilling operations, by the natural pressure of the wells and pumping orcompression operations. Unconventional oil and gas resources are muchmore difficult to extract from the earth, and utilize specializedtechniques, such as hydraulic fracturing. Hydraulic fracturing, or“fracking,” produces fractures in the rock formation that stimulate theflow of oil and natural gas. Unconventional resources include shale oiland gas, tight oil, coal bed methane gas, water soluble gas, tight gassands, and natural gas hydrate.

In the oil and gas industry, significant effort is spent inunderstanding the location, size, and contents of subsurface hydrocarbonreserves, both in land formations and offshore. The development of largeunderground reservoirs often includes the building of computersimulation models, in which oil and gas companies have come to dependupon in order to enhance their ability to exploit their petroleumreserves.

In some instances, modeling of a reservoir proceeds through twophases—history matching and prediction, or forecasting. In the historymatching phase, past production of a field and wells on the field isrepeatedly modeled with variations to the geological model designed toimprove the match between historical data and simulation. Productionforecasts are engineering interpretations of volumetric and physicaldata to predict the performance of hydrocarbon producing (oil and gas)wells. Producing wells with historical data have uncertainty about theirdecline rates as reserves are depleted. The production forecasts aresaved in a database to perform graphical comparison between multipleforecasts and manual input of empirical parameters. This implementationallows engineers to perform dynamic production analysis, which iseffective in determining the future duration of reserves, businessplanning and understanding the economic viability of the well.

Various techniques have been utilized in the industry to attempt todetermine if sufficient oil or gas reserves are present in a givenreservoir to warrant drilling. Petroleum engineers undergo intensivetraining and perform highly skilled and specialized labor to createreservoir simulation models from scratch. Reservoir simulation modelscontain data which describe the specific geometries of the rockformations and the wells, the fluid and rock property data, as well asproduction and injection history of the specific reservoir; injectionreferring to injecting water into an oil and/or gas reservoir tomaintain pressure/voidage replacement. Reservoir simulation models areformed by reservoir simulators on a computer program run on a dataprocessing system, such as a high-performance computing (HPC) system.Oil and gas companies are investing in the infrastructure to empowertheir engineers with the most advanced HPC resources to performsimulation. HPC capabilities, matched with sophisticated modeling andsimulation, amount to extremely high infrastructure costs.

SUMMARY

The present disclosure relates to a method for predicting oil and gasreservoir production including a production analysis system usingmachine learning/neural network model(s) on pre-run numericalsimulations for the evaluation of petroleum reservoir productionperformance.

Machine learning/neural network model(s) is usable to create deeplearning algorithms, which in turn are usable to predict the declinecurve for a specific wellsite. Machine learning is a mathematic approachto forecasting using massive amounts of data to “teach” algorithmspredictable outcomes based on given parameters. Machine learning/neuralnetwork model(s) in some applications are limited by the data that isavailable for training the model, i.e., training data. The availabledata being the field production data for oil and natural gas reservesassociated with a specific wellsite. For example, if a wellsite has onlybeen active for 6 months, then the training set for “teaching” theneural network is limited to 6-month's worth of data. Teaching themodels from simulation results which are pre-run for 30 years along withfull parametrization capabilities allows users to minimize uncertaintiesand maximize profitability for reserves in current and future drilledwells.

The decline curve estimates are predicted by using factors taken fromthe wellsite data including, but not limited to: Initial ProductionWater (bbl), Initial Production Oil (bbl), Oil Cumulative Production(bbl), Oil Rate (BOPD), Initial Production Gas (MCF), Gas CumulativeProduction (MCF), Gas Rate (MCF/month), and Well Type.

Decline curve analysis (DCA) is a graphical procedure used for analyzingdeclining production rates and forecasting future performance of oil andgas wells based on past production history. DCA is a tool in analyzingpetroleum and gas production. Some decline curves used in petroleumengineering are Production Rate vs. Time, Cumulative Production vs.Time, and Production Rate vs. Cumulative Production.

Most of the DCAs are based on the empirical Arps equations: exponential,hyperbolic, and harmonic equations. Arps equations are used to predicthydrocarbon reserves and production performance related to oil and gaswells. Most decline curve methods/models are developed on the basis ofan Arps model such as the following example, q(t)=qi/(1+bDit)^1/b, whereq_(t) stands for the total flow rate at time t, q_(i) denotes theinitial flow rate, D_(i) (1/day) expresses the initial decline rate, andb indicates the Arps decline curve exponent.

Arps equations are used due to simplicity and low computational costs.The exponential decline curve tends to underestimate reserves andproduction rates; the hyperbolic and harmonic decline curves have atendency to overpredict the reservoir performance.

The following options for type curve analysis are able to be selectedfor best fit based upon measurements and the user's preference. Theoptions of exponential, hyperbolic, or harmonic curve functions and inaddition the choice of multi segment Arps, Fetkovich-Arps types,Bayestan Probabilistic Decline Curve Analysis, Fetkovich, Blasingame andAganval-Gardner type curve methods, Duong decline, Modified Duong'smodel, Multi-segment decline, Power law decline (ilk), Logistic growthmodel, Gringarten type curve analysis, Stretched exponential decline,Agarwal-Gardner type curve analysis, mechanistic Li-Home model, orWattenharger type curve analysis.

Type Wells are used in creating appropriate analogues to use inproduction forecasting. The industry constructs a Type Well to determinea simple arithmetic average production rate at selected times fromproducing wells. Type Wells are used for evaluating reserves, productionperformance, and optimization analysis. Type Wells represent an averagebehavior production forecasting profile for a collection of wells for aspecified duration or area.

The present disclosure provides a computerized method for determiningwell performance, in which the program is capable of processing datausing machine learning/neural network(s) to create deep learning modelslearning from pre-run simulations, to provide reliableproduction/reserves estimates.

Additionally, the present disclosure provides a method capable ofmodeling and implementing operations based on a complex analysis of awide variety of specific parameters affecting oil and gas production,while minimizing errors in production forecasting and booking reservesthat directly impact company financial performance.

The present disclosure incorporates a more dependable, efficient, andaccurate reservoir production analysis and predictive method usingmachine learning/neural network(s) and simulation models to determinereliable estimates of well production, such as the one described herein.

Furthermore, the present disclosure provides a petroleum reservoirproduction modeling system that incorporates a production analysissystem for the evaluation of petroleum reservoir production performance,such as the one described herein.

In at least one embodiment of the present disclosure is a reservoirproduction modeling and forecasting system that incorporates aproduction analysis system using machine learning/neural network modelslearning from pre-run simulation results for the evaluation of petroleumreservoir production performance.

The method of the present disclosure further provides clients with amethod for analyzing case study evaluations for type well matching,optimization in well spacing and timing, as well as maximizingefficiency and operational performance.

The method of the present disclosure further provides client assistanceby scientifically producing a valuation/bid for an asset, such as landcontaining oil or gas, in order to determine whether the development ofa reservoir should be pursued in terms of buying or selling the asset.

In at least one embodiment of the present disclosure, a computerimplemented method in simulation containing a commercializedphysics-based forecasting tool for conventional and unconventional oiland gas, provides a user with the ability to generate hundreds ofthousands of simulations from the deep learning models stored in thecloud with actual wellsite parameters and actual wellsite productiondata, and use machine learning to create deep learning algorithms andneural networks for more accurate simulations and modeling.

The present disclosure provides for precise forecast production andestimate reserves to maximize profitability and effectively andefficiently increase the predictability of oil and gas reservoirproduction by evaluating the performance of well production through themethod described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For an understanding of embodiments of the disclosure, reference is nowmade to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a diagram of a wellsite for conventional and unconventionaloil and gas, represented by a geological image, in accordance with atleast one embodiment of the present disclosure.

FIG. 2 is a diagram of a wellsite for conventional and unconventionaloil and gas, in accordance with at least one embodiment of the presentdisclosure.

FIG. 3 is a diagram of a geological image of directional drilling, inaccordance with at least one embodiment of the present disclosure.

FIG. 4 is a diagram of a user interface for a subsurface parameters'analysis, in accordance with at least one embodiment of the presentdisclosure.

FIG. 5 is a diagram of a user interface for shale assessment/future typewells analysis, in accordance with at least one embodiment of thepresent disclosure.

FIG. 6 is a diagram of components of cloud computing, a data processingsystem, in accordance with at least one embodiment of the presentdisclosure.

FIG. 7 is a flowchart of a method for predicting oil and gas reservoirproduction in current producing wells, in accordance with at least oneembodiment of the present disclosure.

FIG. 8 is a flowchart of a method for predicting oil and gas reservoirproduction in future producing wells, in accordance with at least oneembodiment of the present disclosure.

FIG. 9 is a flowchart of a method for predicting oil and gas reservoirproduction, to automate forecasting for current and future producingwells, in accordance with at least one embodiment of the presentdisclosure.

FIG. 10 is a flowchart of a method for predicting oil and gas reservoirproduction using algorithms to teach a deep learning and/or neuralnetwork models from pre-run simulation results, in accordance with atleast one embodiment of the disclosure.

FIG. 11 is a diagram of a user interface of performing machine learningfor well and/or reservoir analysis, in accordance with at least oneembodiment of the present disclosure.

In the Figures, the same reference numerals are used for componentswhich are identical or similar, even if a repeated description issuperfluous for reasons of simplicity.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the disclosure or the application and uses of thedisclosure. As used herein, the word “exemplary” means “serving as anexample, instance, or illustration.” Thus, any embodiment describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. Furthermore, there is no intentionto be bound by any expressed or implied theory presented in thepreceding technical field, background, brief summary, or the followingdetailed description.

For ease of reference, certain terms used in this application and theirmeanings as used in this context are set forth. To the extent a termused herein is not defined below, it should be given the broadestdefinition persons in the pertinent art have given that term asreflected in at least one printed publication or issued patent. Further,the present disclosure is not limited by the usage of the terms shownbelow, as all equivalents, synonyms, new developments, and terms ormethods that serve the same or a similar purpose are considered to bewithin the scope of the present claims.

In this description, reference is made to the drawings, wherein likeparts are designated with like reference numerals throughout. As used inthe description herein and throughout, the meaning of “a,” “an,” “the,”and “said” includes plural reference unless the context clearly dictatesotherwise. Also, as used in the description herein, the meaning of “in”includes “into” and “on” unless the context clearly dictates otherwise.

“Analytical software” refers to data analysis software. An examplepertinent to the present disclosure includes but is not limited toSpotfireTM. The analytical software includes a parameters window,wherein the user is able to define the ranges of the specifiedparameters in the parameters window.

As used in this description, the terms “component,” “database,”“module,” “system,” and the like are intended to broadly capture acomputer-related entity, either hardware, firmware, a combination ofhardware and software, software, or software in execution. For example,a component, in some instances, is a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. An example of a database pertinent to the presentdisclosure includes but is not limited to a Relational Database System.

“Decline curve model” refers to employing the graphical procedure ofdecline curve analysis. An example pertinent to the present disclosureincludes but is not limited to Arps decline curve analysis.

“Decline curve parameters” refer to decline rate, exponential, b factor,Arps, (super) hyperbolic, harmonic, and terminal decline rate. In atleast some embodiments, the generated decline curve is exponential. Inat least some embodiments, the generated decline curve is hyperbolic. Inat least some embodiments, the generated decline curve is harmonic. Inat least some embodiments, the generated decline curve includes one ormore curve segments, and each curve segment includes unique declinecurve parameters. An example of decline curve parameters pertinent tothe present disclosure includes but is not limited to Arps parameters.

“Areas of Interest” refers to a geological area which warrants drilling,based on specific parameter values over which the user is able tocontrol.

“Outcome” includes a goal or objective of an optimization process. In atleast some embodiments, an outcome includes a set of simulation codesand/or algorithms. In at least some embodiments, an outcome includes theerrors or uncertainty in predictions of future production, includingspecific parameter values over which the user is able to control. In atleast some embodiments, the outcome determines one or more actions to beapplied to the operation of the system, in which the operation isadjusted to perform in a manner that most closely meets the goals orobjectives of the user.

“History matching” refers to the process of adjusting unknownparameters, such as the ones described below, of a reservoir model untilthe predictions of the model resemble the past production of thereservoir as closely as possible. The more historical data in the basecase that is provided for history matching, the more reliable the“simulation curve” of the present disclosure will be, which serves as abasis for history matching error determination and the reliability offuture performance predictions. History matching is extremely timeconsuming and highly dependent on the skill and knowledge of a reservoirengineer.

“Geological model” is a computer-based representation of a subsurfaceearth structure, representative of the structure and the behaviorthereof. Geological models are used in the optimization and developmentof a reservoir to determine structural and petrophysical properties of areservoir.

Examples of geological model parameters pertinent to the presentdisclosure include but are not limited to the following: stratigraphicsurfaces, flooding surfaces, structural surfaces, boundaries, well data,lithofacies, porosity, permeability, sequence interfaces, fluidcontacts, fluid saturation, seismic trace data, subsurface faults,bounding surfaces, and facies variations.

“Production Data” refers to any values that are able to be measured overthe life of the field. Examples include rates of production of oil, gas,and water from individual producing wells, pressure measured vs. depthfor specified wells at specified times, pressure at a specified depthmeasured in a specified well vs. time, seismic response measured at aspecified time over a specified area, fluid compositions vs. time inspecified wells, flow rate vs. depth for a specified well at specifiedtimes.

“Reserves” refers to the estimated quantities of oil and gas to beproduced from the current date to the end of life of the well, whichgeological and engineering data demonstrate with reasonable certainty tobe recoverable in future years from known reservoirs.

“Reservoir simulation model,” “simulation model,” “simulation curves”and the like refer to a mathematical representation of a hydrocarbonreservoir and the fluids, wells, and facilities associated with thehydrocarbon reservoir. Simulation curves are used to conduct numericalexperiments regarding future performance of the hydrocarbon reservoir todetermine the most profitable operating strategy. A petroleum engineermanaging a hydrocarbon reservoir is able to create many differentsimulation models to quantify the past performance of the reservoir andpredict future performance of the reservoir.

“Wellsite” refers to a wellbore penetrating a subterranean formation forextracting fluid from an underground reservoir therein.

In analysis methods according to at least one embodiment of the presentdisclosure, production forecast models are generated using reservoirsimulation software such as Computer Modelling Group™ reservoirsimulation software or Petrel Reservoir Engineering Eclipse™ simulationsoftware. In at least some embodiments, different production forecastmodels are able to be used; such other production forecast modelsutilize substitution of or modification of some or all of the belowlisted attributes for the respective production forecast model'sspecific parameters.

In analysis methods according to at least some embodiments of thepresent disclosure, specified parameters, also called attributes aredefined. Examples of specified parameters pertinent to the presentdisclosure include but are not limited to the following: initialreservoir pressure, reservoir depth, bottom-hole flowing pressure,bubble point pressure, dew point pressure, shear stress gradient,pressure gradient, reservoir temperature, reservoir thickness, oildensity, gas gravity, rock matrix and natural fracture permeability,non-fracture zone matrix permeability multiplier, vertical andhorizontal permeability multipliers, rock matrix/natural fractureporosity, natural fracture spacing, rock matrix/hydraulic fractureinitial water saturation, water-oil contact depth, matrix/naturalfracture compressibility, well lateral length, cluster spacing, wellspacing, number of clusters, hydraulic fracturehalf-length/height/width/conductivity/permeability, number of fracturestages, hydraulic fracture compaction/relative permeability tables, andPressure-Volume-Temperature (PVT) tables. The ranges of the specifiedparameters comprise a low and high variable, varied by source.

These data are collected from a variety of public or private sources andare used in the generation or prediction of decline curves as describedby embodiments herein. Examples of data sources pertinent to the presentdisclosure include but are not limited to the following: Google®,Drilling Info, IHS Markit™, Society of Petroleum Engineer Publications™,Wolfcamp, Niobrara, Bonespring, Avalon, Lower Spraberry Shale, Jo Mill,Middle Spraberry, Cline, Tuscaloosa, Mancos, Eagle Ford, Bakken, Avalon,Scoop/Stack, Marcellus, Haynesville, Utica, Fayetteville, Barnett,Woodford, and Woodford-Barnett.

The present disclosure provides a user the ability to generate thousandsof simulations from the integration of numerical and neural networkmodels. In these simulations, there are various parameters associatedwith a single well or plurality of wells. A single well is one that hasno adjacent wells. A plurality of wells, in some instances, is called afamily, a family having at least one parent well and child well. Thevarious parameters include aforementioned actual wellsite parameterswhich are able to be selected for a single well or family of wells todetermine the estimated ultimate recovery (EUR) of each well. By alreadyhaving actual wellsite parameters, however, new parameters are createdbased on relationships between wells. Relationship between wells refersto the spatial distance/position, or well spacing, between at least twowells, well interference, timing and pressure communications.

The new parameters include “NumberTopWells”, “AvgTopDistance”,“AvgTopTiming”, “FdiTop”, “NumberBottomWells”, “AvgBotDistance”,“AvgBotTiming”, “FdiBottom”, “LeftWellDistance”, “LeftWellTimingDiff”,“LeftWellFdi”, “RightWellDistance”, “RightWellTimingDiff”, and“RightWellFdi”. “NumberTopWells” is expressed in units of well countsand describes the number of wells closest to the top within a family ofwells. “AvgTopDistance” is expressed in units of feet and describes theaverage distance of nearest top wells. “AvgTopTiming” is expressed inunits of months and describes the average timing difference of wellsnearest the top. “FdiTop” is expressed in units of square feet timeshydraulic fracture permeability and describes how top wells affect theEUR.

“NumberBottomWells” is expressed in units of well counts and describesthe number of wells closest to the bottom within a family of wells.“AvgBotDistance” is expressed in units of feet and describes the averagedistance of nearest bottom wells. “AvgBotTiming” is expressed in unitsof months and describes average timing difference of wells nearest thebottom. “FdiBottom” is expressed in units of square feet times hydraulicfracture permeability and describes how bottom wells affect the EUR.“LeftWellDistance” is expressed in units of feet and describes thedistance between the target well and the left closest well of the targetwell. “LeftWellTimingDiff” is expressed in units of months and describesthe timing difference of the left closest well of the target well.“LeftWellFdi” is expressed in units of square feet times hydraulicfracture permeability and describes how the left closest well affectsthe EUR.

“RightWellDistance” is expressed in units of feet and describes thedistance between the target well and the right closest well of thetarget well. “RightWellTimingDiff” is expressed in units of months anddescribes the timing difference of the right closest well of the targetwell. “RightWellFdi” is expressed in units of square feet timeshydraulic fracture permeability and describes how the right closest wellaffects the EUR. In addition to the new parameters, the pressure dropper hour (“PDPH”) for a well is able to be calculated.

Another group of parameters referred to as Neighboring Well Influence(“NWI”) parameters are able to be derived from the parameters referredto above using the following equation:

${{NWI}({neighbor})} = {\sum_{n = 1}^{\#{NumWells}}\left( \frac{{Lateral}*{Frac}{Height}*{Xf}*{HFPerm}*{Pi}*{PDPH}}{{BHPi}*{BHP}\min*{Distance}*{Time}{Difference}} \right)}$

Lateral refers to well lateral length in units of feet. Frac Heightrefers to well fracture height in units of feet. Xf refers to fracturehalf-length horizontally in units of feet. HFPerm refers to hydraullicfracture permeability in units of millidarcy. Pi refers to initialreservoir pressure in units of pounds per square inch. PDPH refers topressure drop per hour in units pounds per square inch per hour. BHPirefers to initial bottomhole pressure in units of pounds per squareinch. BHPmin refers to minimum bottomhole pressure in units of poundsper square inch. Distance refers to horizontal or vertical spacingdistance to neighbor well(s) in units of feet. TimeDifference refers toage differences between primary and infill wells in units of years.“TopNWI” is expressed in units of millidarcy times square feet persquare hour and describes how neighboring top wells physically affectthe EUR vertically. “BottomNWI” is expressed in units of millidarcytimes square feet per square hour and describes how neighboring bottomwells physically affect the EUR vertically. “LeftNWI” is expressed inunits of millidarcy times square feet per square hour and describes howneighboring left wells physically affect the EUR horizontally.“RightNWI” is expressed in units of millidarcy times square feet persquare hour and describes how neighboring right wells physically affectthe EUR horizontally.

Another group of parameters referred to as Fracture Driven Interactions(“FDI”) parameters represent how a server fracture interference affectsthe future production for a given well of interest and are measured inoverlapped volume percentages. These FDI parameters are able to becalculated based on the following equation:

${{FDI}{factor}} = \frac{{Intersected}{Volume}}{{Well}{of}{Interest}{Volume}}$

“TopFDI Factor” is expressed in units of percentage and describes thelevel of FDI's influence from the top wells. Intersected Volume refersto volume of intersected rectangular prism in units of feet cubed. Wellof Interest Volume refers to total stimulated rock volume in units offeet cubed. “BotFDI Factor” is expressed in units of percentage anddescribes the level of FDI's influence from the bottom wells. “RightFDlFactor” is expressed in units of percentage and describes the level ofFDI's influence from the right wells. “LeftFDI Factor” is expressed inunits of percentage and describes the level of FDI's influence from theleft wells.

The new parameters are utilized as input features in a neural networkmodel, which determines the output, which is a cumulative oil outputprojection up to a period of 360 months. The cumulative oil output isable to be segmented into cumulative oil outputs for each month startingat month 1 to consecutive months, and up to month 360. In someembodiments, cumulative outputs for secondary phases such as water andnatural gas are determined using a neural network model, as well.

The neural network model is used to build a deep learning model. Tobuild a deep learning model, a computer programming language is used,such as the Python programing language. Keras is a deep learningApplication Programming Interface (“API”) written in Python, and runs ontop of a machine learning platform. A machine learning platformcompatible with Python is, for example, TensorFlow. Using Keras,hypothetical or training parameters, or hyperparameters are tuned totrain a sequential model in order to build an optimal model.

Tuning a parameter refers to training or optimizing a model'sperformance without overfitting the data. The training parameters areentered in an input layer, the input layer having up to 27 nodesrepresenting up to 20 to 50 input features; an output layer having up to359 nodes representing up to 359 months of EUR; and hidden layers tofind the optimal number(s) of nodes in each of the layers. In someembodiments, other parameters are tuned for training purposes, includingoptimizer functions, activation functions, learning rates, dropoutrates, and regularization.

After building a sequential model, a dataset is then adapted to fit thesequential model. In adapting the dataset to fit the sequential model,cross-validation is performed for every tuning. K-fold cross-validationrefers to evaluating a model(s) using a limited sample to estimate howthe model is expected to perform in general when used to makepredictions on data not used during the training of the model(s).Different combinations of parameters are adapted to fit the model, andthe model is able to be trained multiple times, for example, a model istrained ten times (K=10, the data will be split 10 times into a trainingdata set, validation data set, and test data set) using training data,the model undergoing k-fold cross-validation, then tested for accuracyusing test data. 90% of the data being adapted to fit the model istraining data. The remaining 10% of the data is actual test data.

After running the test data through the model, an averagevalidation-loss is determined (MAE or MSE). With training the model tentimes, and tuning the parameters 1000 times, for example, a result of10,000 combinations are used to train the model. An early-stop functionis added to prevent overfitting from occurring, while still obtaining amodel with the lowest possible average-validation loss. Overfittingrefers to a model that models the training data too well, such that themodel learns too much detail or noise, ultimately having a negativeimpact on the model's ability to generalize. An early stop function is atype of regularization which is used to avoid overfitting when traininga learning model repetitively.

After fitting a dataset to a model, a final deep learning model with thelowest possible average-validation loss is generated. The deep learningmodel is saved and uploaded to a cloud server or virtual machine.

When a user wants to perform an analysis, a request is sent to thesystem with the user's defined well parameters, well count, landingtargets, vertical spacing and lateral spacing. The measures for verticalspacing and lateral spacing are represented in exact values or in arange of values. Based on what the user provides for the defined wellparameters, calculations are performed to generate new parameters tomatch the inputs of the deep learning model. The model is loaded fromthe cloud server/virtual machine with the user's defined parameters, anda result is returned. Decline curve analysis is performed on the result,where the curves are drawn using an application, such as Spotfire©referred to above.

On the user side, the system has a graphical user interface. On thegraphical user interface, a user interacts with a homepage. From thehomepage, for example, a user downloads simulated cases saved in adatabase. Each simulated case is a 30 years' time-series of informationassociated with a well saved in a database. To download the time-seriesinformation associated with a well, a user picks what kind of model typefor various areas of interest, the model type being “Single” or“Multiple”.

For a “Single” type model, single-type input parameters are utilized,these parameters including “Formation Name”, “Lateral Length”, “GOR”,“Pi”, “Matrixporo”, “Matrixperm”, “EUR”, and “SWI”.

For a “Multiple” type model, single-type input parameters are utilizedin addition to the following multiple-type input parameters so that aThree-Dimensional model of the user's model is generated. Theseparameters include “Well Count”, “Landing Target”, “Horizontal Spacing”,“Vertical Spacing”, and “Timing”.

After building a Three-Dimensional model, a user inputs the rangeparameters of the model to download all cases of the model. When all theutilized inputs are filled, the SBF software sends an object request toan API stored in the cloud. The API reads through the object request,then finds the matching cases, and returns the matching cases to thesoftware as a data file, such as a json file. In addition to json files,the software reads other data file types. The software converts the jsonfile into the data to be stored in the data table. Using the data file,a History Matching page is selected from the graphical user interface,and on the History Matching page, matching is performed to fit thecurves to their actual wells. These matched cases are saved, and arecord is exported or used as the parameter range to do predictionanalysis for a new model.

To build forecasting cases from the integrated neural network andsimulation models, parameter variables are selected, such as “LandingTarget”, “Landing Distance”, “Well Count”, and “Well Spacing”. To buildvarious drilling scenarios, the position of each well is adjustable bymoving the well model in up, down, left, or right directions to specificcoordinates. The model is shaped by staggering the floors of the wellsor adding/deleting a selected well from the model.

After the new model is designed, input parameters for each well areselected for the model. A user is able to select two options for theinput parameters: (i) recorded simulation cases or (ii) type-in inputparameters. The following parameters, at least some of which have beenreferred to above, are selected for a model, and include Lateral Length,Well Spacing, Pb, Pi, Xf, Swi, HFSwi, HFPerm, Fracture Penetration Up,Fracture Penetration Down, Matrixporo, Matrixperm, Perfcluster Spacing,Timing in Months and PDPH (Pressure drop per hour).

In some embodiments, a new model is built based on an existing model. Tobuild a new model based on an existing model, parameter variables areselected, such as “Landing Target”, “Landing Distance”, “Well Count”,and “Well Spacing”. After selecting the parameter variables, theparameter variables are assigned from each matched case to correspondwith each premium well in the model. A range of the parameters is thenset from the premium model for new wells that the user wants to add onto the current model. Prediction is then performed, and trigger APIfunctions discussed above to request deep learning models to predict thetype curve outcomes. Outcomes are displayed as a family of curves orindividual curves.

FIG. 1 is a diagram of a wellsite 150 for conventional 179 andunconventional 178 oil and gas, represented by a geological image.Drilling rigs 155 are pieces of equipment used to create holes orwellbores 156 in the earth's surface 153. Conventional non-associatedgas 159, gas already in the reservoir, does not accumulate withconventional oil 151. Conventional associated gas 152 accumulates inconjunction with the conventional oil 151. The conventional gasaccumulations 152, 159 occurs when gas migrates from oil and gas richshale 157 into sandstone formation 140, which then becomes trapped by anoverlying impermeable formation, called a seal 154. Tight sand gasaccumulations 158 occur when gas migrates from a source rock into thesandstone formation 140 but is unable to migrate upward due to thepermeability in the sandstone. Coalbed methane 141 is generated duringthe transformation of organic material to coal.

FIG. 2 is a diagram of another geological image of the wellsite 150displaying the conventional 179 and the unconventional 178 methods ofdrilling oil and gas. The surface is an onshore or offshore facilityproducing conventional or unconventional hydrocarbons from asubterranean reservoir. The drilling rigs 155 are machines on thesurface used to drill the wellbores 156. The conventional 179 method isthe traditional way of drilling oil and gas, extracted by naturalpressure, to access the conventional non-associated gas 159. Theunconventional 178 method is drilling down the wellbore 156horizontally, causing fracking 177, in order to access the oil and gasrich shale 157.

FIG. 3 is a diagram of a geological image of directional drilling 175.The drilling rigs 155 allow the oil and gas rich shale 157 to beaccessed via horizontal drilling techniques from the wellbore 156.

FIG. 4 is a diagram of a graphic user interface for a subsurfaceparameters' analysis 180, according to the present disclosure. Thesubsurface parameters' analysis 180 shows the decline curve analysisthat appears to a user on his or her display. A window for selected wellinformation 120 appears in the upper left of the screen. The user hasthe ability to select different variables, available to the user, suchas reservoir properties 123, rock and fluid properties 124, wellcompletion specification data 125, and planar hydraulic fracturespecification 126. The graphs appearing to the right of the window forthe selected well information 120 includes a graph of an oil rate vs.time simulation 121 and a graph of a cumulative oil production vs. timesimulation 122, in accordance with an exemplary embodiment of thepresent disclosure.

FIG. 5 is a diagram of a graphic user interface for shaleassessment/future type wells analysis 190, according to the presentdisclosure. In the upper left of the screen appears a window for casespecific data 130 that includes the ranges of the specified parametersfor the reservoir properties 123 and the well completion specificationdata 125. The ranges of the specified parameters comprise a low and highvariable, varied by source. Available graphs appearing in the upperright of the screen are a graph of an oil cumulative production oil ratesimulation 132 and a graph of a gas cumulative production gas ratesimulation 133. Graphs appearing in the lower right of the screen are agraph of an oil cumulative production vs. time simulation 134 and agraph of a gas cumulative production vs. time simulation 135. In thelower left of the screen appears a window for a list of available caseswith desired reservoir and well characterization 131.

FIG. 6 is a flowchart of at least one embodiment of the components ofcloud computing, a data processing system 160, according to the presentdisclosure. The data processing system 160 includes one or morecomputers 168, one or more databases 161, and one or more networks 163.The one or more databases 161 contains a plurality of simulation curves162. The plurality of simulation curves 162 is matched to actualwellsite data 167 using high performance computing, containing a virtualserver 166 and a virtual private cloud 165. The desired outcome isuploaded to the one or more networks 163 and stored in the one or morecomputers 168. A client firewall 169 contains the actual wellsite data167 uploaded locally by a user. User input parameters and developmentscenarios and request neural network model 164 to generate simulatedtype curves and display on the one or more computers 168. The dataprocessing system 160 has associated therewith the one or more databases161, the plurality of simulation curves 162, the neural network model164, the virtual server 166, and the virtual private cloud 165,according to the data processing methodology of FIG. 6 .

FIG. 7 , FIG. 8 , and FIG. 9 are flowcharts of a block diagram of amethod for predicting oil and gas reservoir production. A set of data iscollected 200 to generate ranges of specified parameters for one or moreoil and gas reservoirs in order to create a base case 201. The base caseis created 201 in a simulation software using the set of data collected200. Simulation is run on the base case 202. The specified parametersfor the base case are then adjusted 203. The ranges of the specifiedparameters and the base case are used to display a plurality of fluidproduction and reserves in the simulation software 204. The ranges ofthe specified parameters are adjusted to obtain an outcome 205. Theoutcome is displayed in a plurality of simulation curves 206. Theplurality of simulation curves is exported to a database 207 and storedin the database 208 for future use.

FIG. 7 is a flowchart of additional steps for predicting oil and gasreservoir production in current producing wells 250. For currentproducing wells, a set of actual wellsite production data, actualwellsite pressure data is uploaded to an analytical software 209. Theactual wellsite parameter data is inputted, and a user selects aplurality of matching simulation curves 210. Simulation production dataand simulation pressure data from the plurality of matching simulationcurves is matched with the actual wellsite production data and theactual wellsite pressure data 211. The outcome 218 is displayedcontaining the plurality of matching simulation curves 212. If the useris unsatisfied with the outcome, the user selects the plurality ofsimulation curves from the outcome 218 and adjusts the plurality ofsimulation curves using the actual wellsite parameter data 219.Simulation is then run for the plurality of simulation curves tooptimize the outcome 220 and the optimized outcome from the plurality ofsimulation curves is uploaded to the analytical software 221.

However, if the user is satisfied with the outcome displayed containingthe matching simulation curves 212, the user proceeds by selecting a setof production cutoff ranges 213. The outcome is displayed containing theplurality of matching simulation curves 214. A plurality of declinecurve models is created containing the outcome 215. The outcome from theplurality of matching simulation curves is matched to the plurality ofdecline curve models by adjusting a plurality of decline curveparameters 216. The user then exports the plurality of decline curvemodels results into a user format 217.

FIG. 8 is a flowchart of additional steps for predicting oil and gasreservoir production in future producing wells 260. In future producingwells, a set of actual wellsite production data for a plurality of wellsis uploaded to an analytical software by the user 222. The ranges of thespecified parameters are inputted for user defined areas of interest223. The plurality of simulation curves is displayed within the rangesof the specified parameters previously input 224. Probabilitydistribution of the plurality of simulation curves and the actualwellsite production data for the plurality of wells is calculated 225and then compared by adjusting the ranges of the specified parametersuntil the outcome is reached 226. Probabilistic type wells arecalculated from the outcome 227 and matched to the plurality ofsimulation curves 228. The plurality of simulation curves and theprobabilistic type wells results are displayed 229. A plurality ofdecline curve models is then created with the outcome 230. The pluralityof simulation curves and the probabilistic type wells are matched to theplurality of decline curve models by adjusting a plurality of declinecurve parameters 231. The plurality of decline curve models' results isthen exported into a user format 232.

FIG. 9 is a flowchart of additional steps for predicting oil and gasreservoir production, to automate forecasting for current and futureproducing wells 270. To automate forecasting for current and futureproducing wells, a set of actual wellsite production data, actualwellsite pressure data, and the ranges of the specified parameters foractual wellsite parameter data is uploaded to an analytical software fora plurality of wells 233. A set of production cutoff ranges is selected234. The user then inputs a specified number of a plurality of matchingsimulation curves for one or more actual wellsites 235. The ranges ofthe specified parameters from the plurality of simulation curves arematched with the ranges of the specified parameters of the actualwellsite parameter data to generate a plurality of matching simulationcurves 236. The simulation production data and simulation pressure datafrom the plurality of simulation curves is matched with the actualwellsite production data and the actual wellsite pressure data 237.

Steps 236 and 237 are repeated until the outcome is obtained for theplurality of wells 238. The plurality of matching simulation curves forthe plurality of wells is displayed 239. A plurality of decline curvemodels is displayed with the outcome 240. The plurality of simulationcurves is matched to the plurality of decline curve models by adjustinga plurality of decline curve parameters for the plurality of wells 241.The plurality of decline curve models results for the plurality of wellsare exported into a user format 242. Matches for the plurality of wellsare grouped for user defined areas of interest 243. The ranges of thespecified parameters from the matches, the plurality of matchingsimulation curves, and the plurality of decline curve models aredisplayed for the user defined areas of interest 244. The ranges of thespecified parameters are adjusted to optimize the outcome 245.Probabilistic type wells are calculated from the plurality of matchingsimulation curves and the plurality of decline curve models 246 and thenmatched by adjusting the plurality of decline curve parameters for theuser defined areas of interest 247. The plurality of decline curveparameters for the plurality of type wells are then exported into a userformat 248.

FIG. 10 is a flow chart of additional steps for predicting oil and gasreservoir production, using pre-run simulations to build a deep learningand/or neural network model(s). As discussed above, the plurality ofsimulation curves is exported to a database and stored for future use.The stored plurality of simulation curves and up to 30 years'time-series data is downloaded as a “Single” or “Multiple” model type toa systems folder 249. After downloading the model type, the simulatednew parameters are selected as input features 250. The input featuresand up to 30 years' time-series data is entered on one line for modeltraining 251. The model is trained using a multi-regression algorithm252. The model is trained and optimized when the model has the lowestnumber of initial average mean square errors for the whole data set 253.The models are trained using different sample percentages, for example,90% of the data is training data, and 10% of the data is test data 254.The most accurate models are selected 255 and uploaded onto a cloudserver or virtual machine 256. The models are configured to scale 257. Auser inputs ranges of parameters of the model to download all productionand pressure curves cases generated from the model 258. The userreceives production and pressure curves from the models 259, and theneither selects the production and pressure curves data in an analyticalsoftware tool 209 to obtain productivity on a current producing well, ormodifies the production and pressure curves data in an analyticalsoftware tool 222 to obtain productivity on a future producing well.

FIG. 11 is a diagram of a user interface 1100 of performing machinelearning for well and/or reservoir analysis, in accordance with at leastone embodiment of the present disclosure. The Landing Target feature1101 of the user interface 1100 permits a user to enter a numberindicating a quantity of landing targets for the simulation modelrepresentative of an actual wellsite arrangement. The landing targetsare able to be viewed along the Y-axis of the chart adjacent thereto,which displays an array of wellsites. A user is able to input any numberindicating the quantity of landing targets, and in the example depictedin FIG. 11 the quantity of landing targets is 3, and in response to theinput of 3 landing targets 3 wellsites are viewable in a row along theY-axis of said chart.

The Landing Distance feature 1102 of the user interface 1100 permits auser to enter a number indicating a distance between each wellsite alongthe Y-axis of said chart. Distance is able to be expressed in aplurality of standard and metric units, and in the embodiment depictedin FIG. 11 each wellsite is placed 50 feet from an adjacent wellsitealong the Y-axis respective the plurality of columns on said chart.

The Well Count feature 1103 of the user interface 1100 permits a user toa enter a number indicating a total count of wellsites within the modelat a given location, and the given location is representative of anactual location. In response to a user entering the landing target 1101and the well count 1103, the system automatically fits the correspondingnumber of rows to the columns of evenly distributed wellsites along theX-axis.

In the example depicted in FIG. 11 , in response to a user inputting 3landing targets 1101 and 15 wells in the well count 1103, the systemautomatically fits the corresponding number of wells in each row, 5wells in each row. The Well Spacing feature 1104 of the user interface1100 permits a user to a enter a number indicating a distance betweeneach wellsite along the X-axis. In this embodiment, a user has enteredthe number 1,000 indicating the distance between each wellsite along theX-axis. Similarly to the landing distance 1102 measurements, themeasurements of the well spacing 1104 is able to be expressed instandard or metric units, and in this example, the wells are spacedapart from each other by a distance of 1,000 feet along X-axis. Usingthe graphic icons 1105, 1106, and 1107, a user is able to adjust,create, or delete any highlighted well location by assigning X and Ycoordinated accordingly.

The Wellsite feature 1109 is a graphical icon of a wellsiterepresentative of a currently producing well or a future producing well.In this example, a user is able to view 15 individual wellsites. A useris able to hover over or click on each wellsite, and in response tohovering over or clicking on the particular wellsite, a user is able toview information unique to the properties of the particular reservoirand wellsite penetrating into the reservoir in the Reservoir Parametersfeature/table 1117 and Well Parameters feature/table 1118, respectively.A variety of information relating to a reservoir and a wellsite areavailable for viewing under these tables/features, the specificinformation discussed above in this disclosure.

The adjustment tool feature 119 permits a user to adjust the spacing ofa wellsite. A user is able to input a distance and manipulate thewellsite horizontally along the X-axis or vertically along the Y-axis toadjust the selected wellsite to be further from or closer to another anadjacent wellsite. For each particular case input in the Selectedfeature 1111 a user is able to view a plurality of parameter features1112. For each parameter of the plurality of parameters, a user is ableto input a minimum range in a Min. feature 1113 and a maximum range in aMax. feature 1114. A user is also able to search and select a saved caseusing graphical search feature 1108. After inputting the minimum and themaximum for a range for each parameter of the plurality of parameters, auser is then able to save the Saved Case 1111, and access at a latertime.

After setting the ranges for the plurality of parameters a user is thenable to visualize an Area Model type 1116, such as a “Delaware Basin”model. Other types of area models include Midland Basin, WillistionBasin, Powder River Basin, etc. . . After the area model feature/type isselected, then the number of models to be generated is entered in theNumber of Model feature 1115. In this example, 100 models will begenerated based on the input, and each one of these models is able to bevisualized as a unique simulation curve upon selection of the Predictionfeature 1120. Each simulation curve 133 and 134 of FIG. 5 is able to beindividually selected by a user, the respective reservoir and wellsiteparameters are also able to be viewed upon selection of the individuallyselected simulation curve.

A method of predicting an output of oil and gas production in ahydrocarbon reservoir of a current and future producing well using aneural network model, includes receiving a data set comprising aplurality of parameters of the hydrocarbon reservoir at a wellsite. Themethod also includes using the data set to generate a plurality ofsimulation curves of the hydrocarbon reservoir, each parameter of theplurality of parameters has a range, and the range is adjustable, andthe wellsite includes a wellbore penetrating a subterranean formation toextract reserves from the hydrocarbon reservoir. The method alsoincludes performing a simulation, based on the range of each saidparameter of the plurality of parameters, of the hydrocarbon reservoir.The method also includes downloading the plurality of simulation curvesinto a local server to prepare training data for training the neuralnetwork model.

The method also includes calculating a plurality of key factors from aneighboring well for at least two wellsites from the simulation. Themethod also includes combining the plurality of key factors with theplurality of simulated parameters to define as input features of theneural network model. The method also includes defining the plurality ofsimulation curves as output features of the neural network model. Themethod also includes tuning the neural network model using a set ofhidden layers between the input features and the output features,wherein the tuning comprises a plurality of tunings.

The method also includes retrieving, for each said tuning, selecteddata, shuffling and splitting the selected data with K-fold crossvalidation, and scaling the selected data using a scaler to obtainscaled data. The method also includes searching for a plurality ofhyperparameters by fitting the scaled data in each said tuning. Themethod also includes applying an early stop function to prevent thetraining from overfitting the selected data to the neural network model.The method also includes processing each said tuning and calculating anaverage error of the neural network model, and saving the average erroras a result.

The method also includes searching for a plurality of hyperparameters byfitting the scaled data in each said tuning, and comparing the resultand selecting an optimal set of hyperparameters of the plurality ofhyperparameters belonging to the neural network model having a lowestvalidation error. The method also includes further training the neuralnetwork model having the lowest validation error with the optimal set ofhyperparameters to obtain an optimized neural network model, and uploadthe optimized neural network model to a virtual server or a virtualprivate cloud. The method also includes uploading from a clientfirewall, by an analytical module, actual wellsite data comprisingactual wellsite production data, actual wellsite pressure data, andactual wellsite parameter data. The method also includes using ananalytical module user interface to input the wellsite parameter dataand select the range of each said parameter of the plurality ofparameters to display the plurality of simulation curves generated fromthe neural network model in a virtual server or a virtual private cloud.

The method also includes matching simulation production data andsimulation pressure data from the plurality of simulation curvesgenerated from the neural network model in the virtual server or thevirtual private cloud with the actual wellsite production data and theactual wellsite pressure data to obtain a plurality of matchingsimulation curves. The method also includes displaying an outcome of theplurality of matching simulation curves on a display in the analyticalmodule. The method also includes storing the plurality of matchingsimulation curves and the plurality of parameters in the analyticalmodule user interface. The method also includes creating a plurality ofhydrocarbon development scenarios in the analytical module userinterface for drilling operation in an area of interest.

The method also includes assigning the stored plurality of parameters tothe wellsite in a hydrocarbon development scenarios of the plurality ofhydrocarbon development scenarios. The method also includes displayingthe plurality of simulation curves generated from the neural networkmodel in the virtual server or the virtual private cloud using theplurality of hydrocarbon development scenarios. The method also includescalculating a probability distribution for an outcome of the pluralityof simulation curves. The method also includes creating a plurality ofdecline curve models with an outcome of calculated probabilitydistribution.

The method also includes matching the outcome of the plurality ofprobability simulation curves to the plurality of decline curve modelsby adjusting a plurality of decline curve parameters. The method alsoincludes exporting the adjusted plurality of decline curve models forthe current and the future producing wells into a user format foreconomic analysis. The method also includes re-selecting the range ofeach said parameter of the plurality of parameters and re-adjusting thehydrocarbon development scenarios for adjusting the probabilitydistribution for the current and the future producing wells untilachieving an optimal economic result. The method also includes using theadjusted probability distribution to perform a drilling operation todrill another wellbore at the hydrocarbon reservoir.

The range has a low variable and a high variable. The method alsoincludes using a simulation module user interface to adjust each saidrange of the plurality of parameters for the simulation to obtain anoutcome of the base case simulation. The method also includes using asimulation module user interface to display a plurality of hydrocarbonproduction and the reserve based on the adjusted range of the pluralityof parameters and the outcome of the simulation. The method alsoincludes using a simulation module user interface to display the outcomeof the simulation in the plurality of simulation curves on a display inthe simulation module.

The method also includes using a simulation module user interface toexport and store the plurality of simulation curves into a database in avirtual server or a virtual private cloud. The plurality of key factorsincludes neighboring well quantities and influence, spacing differences,timing differences, and FDI factors. The tuning includes using a numberof nodes, activation functions, optimizer functions, learning rates,dropout rates, and regularization.

A method of predicting an output of oil and gas production in ahydrocarbon reservoir of a current and future producing well using aneural network model including receiving, by a data collection module, adata set comprising a plurality of parameters of the hydrocarbonreservoir at a wellsite. The method also including using the data set togenerate a plurality of simulation curves of the hydrocarbon reservoir,and each parameter of the plurality of parameters has a range, and therange is adjustable, and the wellsite including a wellbore penetrating asubterranean formation to extract reserves from the hydrocarbonreservoir. The method also including performing a simulation, by asimulation module, based on the range of each said parameter of theplurality of parameters, of the hydrocarbon reservoir. The method alsoincluding downloading the plurality of simulation curves into a localserver to prepare training data for training the neural network model.

The method also including calculating a plurality of key factors from aneighboring well for at least two wellsites from the simulation. Themethod also including combining the plurality of key factors with theplurality of parameters to define as input features of the neuralnetwork model. The method also including defining the plurality ofsimulation curves as output features of the neural network model.

A method of predicting an output of oil and gas production in ahydrocarbon reservoir of a current and future producing well using aneural network model, including receiving, by a data collection module,a data set comprising a plurality of parameters of the hydrocarbonreservoir at a wellsite. The method also including using the data set togenerate a plurality of simulation curves of the hydrocarbon reservoir,and each parameter of the plurality of parameters has a range, and therange is adjustable, and the wellsite including a wellbore penetrating asubterranean formation to extract reserves from the hydrocarbonreservoir. The method also including performing a simulation, by asimulation module, based on the range of each said parameter of theplurality of parameters, of the hydrocarbon reservoir. The method alsoincluding downloading the plurality of simulation curves into a localserver to prepare training data for training the neural network model.

A computer device of predicting an output of oil and gas production in ahydrocarbon reservoir of a current and future producing well using aneural network model, including a non-transitory computer readablemedium configured to store computer executable instructions. The devicealso includes at least one processor, wherein in response to executingthe computer executable instructions, the processor is configured toreceive a data set, using a graphic user interface (GUI), comprising aplurality of parameters of the hydrocarbon reservoir at a wellsite. Theprocessor is also configured to use the data set to generate a pluralityof simulation curves of the hydrocarbon reservoir on the GUI, and eachparameter of the plurality of parameters has a range, and the range isadjustable using the GUI. The wellsite comprises a wellbore penetratinga subterranean formation to extract reserves from the hydrocarbonreservoir.

The processor is also configured to perform a simulation, using the GUI,based on the range of each said parameter of the plurality ofparameters, of the hydrocarbon reservoir. The processor is alsoconfigured to download the plurality of simulation curves into adatabase to prepare training data for training the neural network model.The processor is also configured to calculate a plurality of key factorsfrom a neighboring well for at least two wellsites from the simulation.The processor is also configured to combine the plurality of key factorswith the plurality of parameters to define as input features of theneural network model. The processor is also configured to define theplurality of simulation curves as output features of the neural networkmodel.

The foregoing description of some embodiments of the disclosure has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the disclosure to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings. The specifically described embodiments explain theprinciples and practical applications to enable one ordinarily skilledin the art to utilize various embodiments and with various modificationsas are suited to the particular use contemplated. It should beunderstood that various changes, substitutions, and alterations can bemade hereto without departing from the spirit and scope of thedisclosure.

What is claimed is:
 1. A method of predicting an output of oil and gasproduction in a hydrocarbon reservoir using a neural network model,comprising: receiving a data set comprising a plurality of parameters ofthe hydrocarbon reservoir at a wellsite, wherein the wellsite comprisesa wellbore penetrating a subterranean formation to extract reserves fromthe hydrocarbon reservoir; generating, using the data set, a pluralityof simulation curves of the hydrocarbon reservoir, wherein eachparameter of the plurality of parameters has a range, and the range isadjustable, and performing a simulation, based on the range of each saidparameter of the plurality of parameters, of the hydrocarbon reservoirto generate a plurality of simulation curves; downloading the pluralityof simulation curves into a local server to prepare training data fortraining the neural network model; calculating a plurality of keyfactors from a neighboring well for at least two wellsites from thesimulation; combining the plurality of key factors with the plurality ofparameters to define input features of the neural network model; anddefining each of the plurality of simulation curves as output featuresof the neural network model.
 2. The method of claim 1, furthercomprising tuning the neural network model using a set of hidden layersbetween the input features and the output features, wherein the tuningcomprises a plurality of tunings.
 3. The method of claim 2, furthercomprising retrieving, for each said tuning of the plurality of tunings,selected data, shuffling and splitting the selected data with K-foldcross validation, and scaling the selected data using a scaler to obtainscaled data.
 4. The method of claim 3, further comprising searching fora plurality of hyperparameters by fitting the scaled data in each saidtuning of the plurality of tunings.
 5. The method of claim 3, furthercomprising applying an early stop function to prevent the training fromoverfitting the selected data to the neural network model.
 6. The methodof claim 2, further comprising processing each said tuning of theplurality of tunings and calculating an average error of the neuralnetwork model, and saving the average error as a result.
 7. The methodof claim 6, further comprising searching for a plurality ofhyperparameters by fitting the scaled data in each said tuning of theplurality of tunings, and comparing a result and selecting an optimalset of hyperparameters of the plurality of hyperparameters belonging tothe neural network model having a lowest validation error.
 8. The methodof claim 7, further comprising: further training the neural networkmodel having the lowest validation error with the optimal set ofhyperparameters to obtain an optimized neural network model; anduploading the optimized neural network model to a virtual server or avirtual private cloud.
 9. The method of claim 1, further comprising:uploading from a client firewall, by an analytical module, actualwellsite data comprising actual wellsite production data, actualwellsite pressure data, and actual wellsite parameter data; andinputting the wellsite parameter data and selecting the range of eachsaid parameter of the plurality of parameters to display the pluralityof simulation curves generated from the neural network model in avirtual server or a virtual private cloud.
 10. The method of claim 9,further comprising: matching simulation production data and simulationpressure data from the plurality of simulation curves generated from theneural network model in the virtual server or the virtual private cloudwith the actual wellsite production data and the actual wellsitepressure data to obtain a plurality of matching simulation curves;displaying an outcome of the plurality of matching simulation curves ona display; and storing the plurality of matching simulation curves andthe plurality of parameters.
 11. The method of claim 9, furthercomprising: creating a plurality of hydrocarbon development scenarios inthe analytical module user interface for drilling operation in an areaof interest; assigning the stored plurality of parameters to thewellsite in a hydrocarbon development scenarios of the plurality ofhydrocarbon development scenarios; and displaying the plurality ofsimulation curves generated from the neural network model in the virtualserver or the virtual private cloud using the plurality of hydrocarbondevelopment scenarios.
 12. The method of claim 11, further comprisingcalculating a probability distribution for an outcome of the pluralityof simulation curves.
 13. The method of claim 12, further comprising:creating a plurality of decline curve models with an outcome ofcalculated probability distribution; matching an outcome of theplurality of probability simulation curves to the plurality of declinecurve models by adjusting a plurality of decline curve parameters; andexporting the adjusted plurality of decline curve models for the currentand the future producing wells into a user format for economic analysis.14. The method of claim 11, further comprising: re-selecting the rangeof each said parameter of the plurality of parameters and re-adjustingthe hydrocarbon development scenarios for adjusting the probabilitydistribution for the current and the future producing wells untilachieving an optimal economic result; and using the adjusted probabilitydistribution to select a location to perform a drilling operation todrill another wellbore at the hydrocarbon reservoir.
 15. The method ofclaim 1, wherein the range has a low variable and a high variable. 16.The method of claim 1, further comprising using a simulation module userinterface to: adjust each said range of the plurality of parameters forthe simulation to obtain an outcome of the base case simulation; displaya plurality of hydrocarbon producing wells and the reserve based on theadjusted range of the plurality of parameters and the outcome of thesimulation; display an outcome of the simulation in the plurality ofsimulation curves on a display in the simulation module; and export andstore the plurality of simulation curves into a database in a virtualserver or a virtual private cloud.
 17. The method of claim 1, whereinthe plurality of key factors comprises: neighboring well quantities andinfluence, spacing differences, timing differences, or FDI factors. 18.The method of claim 2, wherein the tuning further comprises using: anumber of nodes, activation functions, optimizer functions, learningrates, dropout rates, and regularization.
 19. A method of predicting anoutput of oil and gas production in a hydrocarbon reservoir using aneural network model, comprising: receiving, by a data collectionmodule, a data set comprising a plurality of parameters of thehydrocarbon reservoir at a wellsite, wherein the wellsite comprises awellbore penetrating a subterranean formation to extract reserves fromthe hydrocarbon reservoir; generating, using the data set, a pluralityof simulation curves of the hydrocarbon reservoir, wherein eachparameter of the plurality of parameters has a range, and the range isadjustable; performing a simulation, by a simulation module, based onthe range of each said parameter of the plurality of parameters, of thehydrocarbon reservoir to generate a plurality of simulation curves;downloading the plurality of simulation curves into a local server toprepare training data for training the neural network model; calculatinga plurality of key factors from a neighboring well for at least twowellsites from the simulation; combining the plurality of key factorswith the plurality of parameters to define input features of the neuralnetwork model; and defining each of the plurality of simulation curvesas output features of the neural network model.
 20. A computer device ofpredicting an output of oil and gas production in a hydrocarbonreservoir using a neural network model, comprising: a non-transitorycomputer readable medium configured to store computer executableinstructions; at least one processor, wherein in response to executingthe computer executable instructions, the processor is configured to:receive a data set, using a graphic user interface (GUI), comprising aplurality of parameters of the hydrocarbon reservoir at a wellsite,wherein the wellsite comprises a wellbore penetrating a subterraneanformation to extract reserves from the hydrocarbon reservoir; generate,using the data set, a plurality of simulation curves of the hydrocarbonreservoir on the GUI, wherein each parameter of the plurality ofparameters has a range, and the range is adjustable using the GUI;perform a simulation, using the GUI, based on the range of each saidparameter of the plurality of parameters, of the hydrocarbon reservoir;download the plurality of simulation curves into a database to preparetraining data for training the neural network model; calculate aplurality of key factors from a neighboring well for at least twowellsites from the simulation; combine the plurality of key factors withthe plurality of parameters to define input features of the neuralnetwork model; and define each of the plurality of simulation curves asoutput features of the neural network model.